code: def test_distrib_integration(distributed):
      rank = idist.get_rank()
      torch.manual_seed(41 + rank)
      n_batches, batch_size = 5, 10
      y = torch.randint(0, 2, size=(n_batches * batch_size,))
      y_pred = torch.rand((n_batches * batch_size,))
  
      def update(engine, i):
          return (
              y_pred[i * batch_size : (i + 1) * batch_size],
              y[i * batch_size : (i + 1) * batch_size],
          )
  
      engine = Engine(update)
  
      device = (
          torch.device("cpu")
          if idist.device().type == "xla"
          else idist.device()
      )
      metric = RocCurve(device=device)
      metric.attach(engine, "roc_curve")
  
      data = list(range(n_batches))
  
      engine.run(data=data, max_epochs=1)
  
      fpr, tpr, thresholds = engine.state.metrics["roc_curve"]
  
      assert isinstance(fpr, torch.Tensor) and fpr.device == device
      assert isinstance(tpr, torch.Tensor) and tpr.device == device
      assert (
          isinstance(thresholds, torch.Tensor)
          and thresholds.device == device
      )
  
      y = idist.all_gather(y)
      y_pred = idist.all_gather(y_pred)
      sk_fpr, sk_tpr, sk_thresholds = roc_curve(
          y.cpu().numpy(), y_pred.cpu().numpy()
      )
  
      np.testing.assert_array_almost_equal(fpr.cpu().numpy(), sk_fpr)
      np.testing.assert_array_almost_equal(tpr.cpu().numpy(), sk_tpr)
      np.testing.assert_array_almost_equal(
          thresholds.cpu().numpy(), sk_thresholds
      )
  
file_path: ignite/tests/ignite/contrib/metrics/test_roc_curve.py
imports: from unittest.mock import patch
  import numpy
  import pytest
  import sklearn
  import torch
  from sklearn.metrics import roc_curve
  from ignite import distributed
  from ignite.contrib.metrics.roc_auc import RocCurve
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  from ignite.metrics.epoch_metric import EpochMetricWarning
label_desc: Use image node_id 8 for calling a global function with example usage: test_distrib_integration(distributed) and returns: y_pred,  y